"""simple docstring"""


import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
	import textwrap

	from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
	import torch

	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	a				=False


a				=logging.get_logger(__name__)
a				='ybelkada/fonts'





def      lowerCamelCase_					(   )				->   Union[str, Any]:




						'''simple docstring'''



						if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
												raise ImportError(
												    F'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
												    "Pix2StructImageProcessor. Please upgrade torch."							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Optional[Any]:




						'''simple docstring'''



						requires_backends(__lowerCAmelCase  ,       ["torch"]							)
						_check_torch_version()

						lowerCamelCase__   =image_tensor.unsqueeze(0							)
						lowerCamelCase__   =torch.nn.functional.unfold(__lowerCAmelCase  ,       (patch_height, patch_width)  ,       stride=(patch_height, patch_width)							)
						lowerCamelCase__   =patches.reshape(image_tensor.size(0							)  ,       image_tensor.size(1							)  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       -1							)
						lowerCamelCase__   =patches.permute(0  ,       4  ,       2  ,       3  ,       1							).reshape(
						    image_tensor.size(2							) // patch_height  ,       image_tensor.size(3							) // patch_width  ,       image_tensor.size(1							) * patch_height * patch_width  ,       )
						return patches.unsqueeze(0							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase = 36  ,       __lowerCAmelCase = "black"  ,       __lowerCAmelCase = "white"  ,       __lowerCAmelCase = 5  ,       __lowerCAmelCase = 5  ,       __lowerCAmelCase = 5  ,       __lowerCAmelCase = 5  ,       __lowerCAmelCase = None  ,       __lowerCAmelCase = None  ,       )				->   Image.Image:




						'''simple docstring'''



						requires_backends(__lowerCAmelCase  ,       "vision"							)
						# Add new lines so that each line is no more than 80 characters.

						lowerCamelCase__   =textwrap.TextWrapper(width=80							)
						lowerCamelCase__   =wrapper.wrap(text=__lowerCAmelCase							)
						lowerCamelCase__   ="\n".join(__lowerCAmelCase							)

						if font_bytes is not None and font_path is None:
												lowerCamelCase__   =io.BytesIO(__lowerCAmelCase							)
						elif font_path is not None:
												lowerCamelCase__   =font_path
						else:
												lowerCamelCase__   =hf_hub_download(__lowerCAmelCase  ,       "Arial.TTF"							)
						lowerCamelCase__   =ImageFont.truetype(__lowerCAmelCase  ,       encoding="UTF-8"  ,       size=__lowerCAmelCase							)

						# Use a temporary canvas to determine the width and height in pixels when
						# rendering the text.
						lowerCamelCase__   =ImageDraw.Draw(Image.new("RGB"  ,       (1, 1)  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =temp_draw.textbbox((0, 0)  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)

						# Create the actual image with a bit of padding around the text.
						lowerCamelCase__   =text_width + left_padding + right_padding
						lowerCamelCase__   =text_height + top_padding + bottom_padding
						lowerCamelCase__   =Image.new("RGB"  ,       (image_width, image_height)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =ImageDraw.Draw(__lowerCAmelCase							)
						draw.text(xy=(left_padding, top_padding)  ,       text=__lowerCAmelCase  ,       fill=__lowerCAmelCase  ,       font=__lowerCAmelCase							)
						return image





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       **__lowerCAmelCase							)				->   Union[str, Any]:




						'''simple docstring'''



						requires_backends(__lowerCAmelCase  ,       "vision"							)

						# Convert to PIL image if necessary
						lowerCamelCase__   =to_pil_image(__lowerCAmelCase							)

						lowerCamelCase__   =render_text(__lowerCAmelCase  ,       **__lowerCAmelCase							)
						lowerCamelCase__   =max(header_image.width  ,       image.width							)

						lowerCamelCase__   =int(image.height * (new_width / image.width)							)
						lowerCamelCase__   =int(header_image.height * (new_width / header_image.width)							)

						lowerCamelCase__   =Image.new("RGB"  ,       (new_width, new_height + new_header_height)  ,       "white"							)
						new_image.paste(header_image.resize((new_width, new_header_height)							)  ,       (0, 0)							)
						new_image.paste(image.resize((new_width, new_height)							)  ,       (0, new_header_height)							)

						# Convert back to the original framework if necessary
						lowerCamelCase__   =to_numpy_array(__lowerCAmelCase							)

						if infer_channel_dimension_format(__lowerCAmelCase							) == ChannelDimension.LAST:
												lowerCamelCase__   =to_channel_dimension_format(__lowerCAmelCase  ,       ChannelDimension.LAST							)

						return new_image






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  List[str]    				=			['''flattened_patches''']

				def __init__( self      ,					_lowerCamelCase = True      ,					_lowerCamelCase = True      ,					_lowerCamelCase = None      ,					_lowerCamelCase = 2048      ,					_lowerCamelCase = False      ,					**_lowerCamelCase      ,					):
										super().__init__(**_lowerCamelCase )
										lowerCamelCase__   =patch_size if patch_size is not None else {"height": 16, "width": 16}
										lowerCamelCase__   =do_normalize
										lowerCamelCase__   =do_convert_rgb
										lowerCamelCase__   =max_patches
										lowerCamelCase__   =is_vqa

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					**_lowerCamelCase ):
										requires_backends(self.extract_flattened_patches      ,					"torch" )
										_check_torch_version()

										# convert to torch
										lowerCamelCase__   =to_channel_dimension_format(_lowerCamelCase      ,					ChannelDimension.FIRST )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase )

										lowerCamelCase__   , lowerCamelCase__   =patch_size["height"], patch_size["width"]
										lowerCamelCase__   , lowerCamelCase__   =get_image_size(_lowerCamelCase )

										# maximize scale s.t.
										lowerCamelCase__   =math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
										lowerCamelCase__   =max(min(math.floor(scale * image_height / patch_height )      ,					_lowerCamelCase )      ,					1 )
										lowerCamelCase__   =max(min(math.floor(scale * image_width / patch_width )      ,					_lowerCamelCase )      ,					1 )
										lowerCamelCase__   =max(num_feasible_rows * patch_height      ,					1 )
										lowerCamelCase__   =max(num_feasible_cols * patch_width      ,					1 )

										lowerCamelCase__   =torch.nn.functional.interpolate(
										    image.unsqueeze(0 )      ,					size=(resized_height, resized_width)      ,					mode="bilinear"      ,					align_corners=_lowerCamelCase      ,					antialias=_lowerCamelCase      ,					).squeeze(0 )

										# [1, rows, columns, patch_height * patch_width * image_channels]
										lowerCamelCase__   =torch_extract_patches(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )

										lowerCamelCase__   =patches.shape
										lowerCamelCase__   =patches_shape[1]
										lowerCamelCase__   =patches_shape[2]
										lowerCamelCase__   =patches_shape[3]

										# [rows * columns, patch_height * patch_width * image_channels]
										lowerCamelCase__   =patches.reshape([rows * columns, depth] )

										# [rows * columns, 1]
										lowerCamelCase__   =torch.arange(_lowerCamelCase ).reshape([rows, 1] ).repeat(1      ,					_lowerCamelCase ).reshape([rows * columns, 1] )
										lowerCamelCase__   =torch.arange(_lowerCamelCase ).reshape([1, columns] ).repeat(_lowerCamelCase      ,					1 ).reshape([rows * columns, 1] )

										# Offset by 1 so the ids do not contain zeros, which represent padding.
										row_ids += 1
										col_ids += 1

										# Prepare additional patch features.
										# [rows * columns, 1]
										lowerCamelCase__   =row_ids.to(torch.floataa )
										lowerCamelCase__   =col_ids.to(torch.floataa )

										# [rows * columns, 2 + patch_height * patch_width * image_channels]
										lowerCamelCase__   =torch.cat([row_ids, col_ids, patches]      ,					-1 )

										# [max_patches, 2 + patch_height * patch_width * image_channels]
										lowerCamelCase__   =torch.nn.functional.pad(_lowerCamelCase      ,					[0, 0, 0, max_patches - (rows * columns)] ).float()

										lowerCamelCase__   =to_numpy_array(_lowerCamelCase )

										return result

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None      ,					**_lowerCamelCase ):
										if image.dtype == np.uinta:
																lowerCamelCase__   =image.astype(np.floataa )

										# take mean across the whole `image`
										lowerCamelCase__   =np.mean(_lowerCamelCase )
										lowerCamelCase__   =np.std(_lowerCamelCase )
										lowerCamelCase__   =max(_lowerCamelCase      ,					1.0 / math.sqrt(np.prod(image.shape ) ) )

										return normalize(_lowerCamelCase      ,					mean=_lowerCamelCase      ,					std=_lowerCamelCase      ,					**_lowerCamelCase )






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = ChannelDimension.FIRST      ,					**_lowerCamelCase      ,					):
										lowerCamelCase__   =do_normalize if do_normalize is not None else self.do_normalize
										lowerCamelCase__   =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
										lowerCamelCase__   =patch_size if patch_size is not None else self.patch_size
										lowerCamelCase__   =max_patches if max_patches is not None else self.max_patches
										lowerCamelCase__   =self.is_vqa

										if kwargs.get("data_format"      ,					_lowerCamelCase ) is not None:
																raise ValueError("data_format is not an accepted input as the outputs are " )

										lowerCamelCase__   =make_list_of_images(_lowerCamelCase )

										if not valid_images(_lowerCamelCase ):
																raise ValueError(
																    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																    "torch.Tensor, tf.Tensor or jax.ndarray." )

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																lowerCamelCase__   =[convert_to_rgb(_lowerCamelCase ) for image in images]

										# All transformations expect numpy arrays.
										lowerCamelCase__   =[to_numpy_array(_lowerCamelCase ) for image in images]

										if is_vqa:
																if header_text is None:
																						raise ValueError("A header text must be provided for VQA models." )
																lowerCamelCase__   =kwargs.pop("font_bytes"      ,					_lowerCamelCase )
																lowerCamelCase__   =kwargs.pop("font_path"      ,					_lowerCamelCase )

																if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																						lowerCamelCase__   =[header_text] * len(_lowerCamelCase )

																lowerCamelCase__   =[
																    render_header(_lowerCamelCase      ,					header_text[i]      ,					font_bytes=_lowerCamelCase      ,					font_path=_lowerCamelCase )
																    for i, image in enumerate(_lowerCamelCase )
																]

										if do_normalize:
																lowerCamelCase__   =[self.normalize(image=_lowerCamelCase ) for image in images]

										# convert to torch tensor and permute
										lowerCamelCase__   =[
										    self.extract_flattened_patches(image=_lowerCamelCase      ,					max_patches=_lowerCamelCase      ,					patch_size=_lowerCamelCase )
										    for image in images
										]

										# create attention mask in numpy
										lowerCamelCase__   =[(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]

										lowerCamelCase__   =BatchFeature(
										    data={"flattened_patches": images, "attention_mask": attention_masks}      ,					tensor_type=_lowerCamelCase )

										return encoded_outputs



"""simple docstring"""


from __future__ import annotations

from typing import Any






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase = 0 ):
										lowerCamelCase__   , lowerCamelCase__   =row, column
										lowerCamelCase__   =[[default_value for c in range(_lowerCamelCase )] for r in range(_lowerCamelCase )]

				def __str__( self ):
										lowerCamelCase__   =F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

										# Make string identifier
										lowerCamelCase__   =0
										for row_vector in self.array:
																for obj in row_vector:
																						lowerCamelCase__   =max(_lowerCamelCase      ,					len(str(_lowerCamelCase ) ) )
										lowerCamelCase__   =F'''%{max_element_length}s'''

										# Make string and return
										def single_line(_lowerCamelCase ) -> str:
																nonlocal string_format_identifier
																lowerCamelCase__   ="["
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
																line += "]"
																return line

										s += "\n".join(single_line(_lowerCamelCase ) for row_vector in self.array )
										return s

				def __repr__( self ):
										return str(self )

				def 						_a      ( self      ,					_lowerCamelCase ):
										if not (isinstance(_lowerCamelCase      ,					(list, tuple) ) and len(_lowerCamelCase ) == 2):
																return False
										elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																return False
										else:
																return True

				def __getitem__( self      ,					_lowerCamelCase ):
										assert self.validate_indicies(_lowerCamelCase )
										return self.array[loc[0]][loc[1]]

				def __setitem__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										assert self.validate_indicies(_lowerCamelCase )
										lowerCamelCase__   =value

				def __add__( self      ,					_lowerCamelCase ):
										assert isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										assert self.row == another.row and self.column == another.column

										# Add
										lowerCamelCase__   =Matrix(self.row      ,					self.column )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =self[r, c] + another[r, c]
										return result

				def __neg__( self ):
										lowerCamelCase__   =Matrix(self.row      ,					self.column )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =-self[r, c]
										return result

				def __sub__( self      ,					_lowerCamelCase ):
										return self + (-another)

				def __mul__( self      ,					_lowerCamelCase ):

										if isinstance(_lowerCamelCase      ,					(int, float) ):  # Scalar multiplication
																lowerCamelCase__   =Matrix(self.row      ,					self.column )
																for r in range(self.row ):
																						for c in range(self.column ):
																												lowerCamelCase__   =self[r, c] * another
																return result
										elif isinstance(_lowerCamelCase      ,					_lowerCamelCase ):  # Matrix multiplication
																assert self.column == another.row
																lowerCamelCase__   =Matrix(self.row      ,					another.column )
																for r in range(self.row ):
																						for c in range(another.column ):
																												for i in range(self.column ):
																																		result[r, c] += self[r, i] * another[i, c]
																return result
										else:
																lowerCamelCase__   =F'''Unsupported type given for another ({type(_lowerCamelCase )})'''
																raise TypeError(_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =Matrix(self.column      ,					self.row )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =self[r, c]
										return result






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										assert isinstance(_lowerCamelCase      ,					_lowerCamelCase ) and isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										assert self.row == self.column == u.row == v.row  # u, v should be column vector
										assert u.column == v.column == 1  # u, v should be column vector

										# Calculate
										lowerCamelCase__   =v.transpose()
										lowerCamelCase__   =(v_t * self * u)[0, 0] + 1
										if numerator_factor == 0:
																return None  # It's not invertable
										return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





	def      lowerCamelCase_					(   )				->   None:




							'''simple docstring'''



							lowerCamelCase__   =Matrix(3  ,       3  ,       0							)
							for i in range(3							):
													lowerCamelCase__   =1
							print(F'''a^(-1) is {ainv}'''							)
							# u, v
							lowerCamelCase__   =Matrix(3  ,       1  ,       0							)
							lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =1, 2, -3
							lowerCamelCase__   =Matrix(3  ,       1  ,       0							)
							lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =4, -2, 5
							print(F'''u is {u}'''							)
							print(F'''v is {v}'''							)
							print(F'''uv^T is {u * v.transpose()}'''							)
							# Sherman Morrison
							print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase  ,       __lowerCAmelCase							)}'''							)





	def      lowerCamelCase_					(   )				->   None:




							'''simple docstring'''



							import doctest

							doctest.testmod()





	testa()


